{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpreting Bi-LSTM Sentiment Classification Models With Integrated Gradients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook loads the pretrained Bi-LSTM model following [PaddleNLP TextClassification](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/examples/text_classification/rnn) and performs sentiment analysis on reviews data. The full official PaddlePaddle sentiment classification tutorial can be found [here](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/examples/text_classification). \n",
    "\n",
    "Interpretations of the predictions are generated and visualized using Integrated Gradients algorithm, specifically the `IntGradNLPInterpreter` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import paddle\n",
    "import numpy as np\n",
    "import interpretdl as it\n",
    "from interpretdl.data_processor.visualizer import VisualizationTextRecord, visualize_text"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have't done so, please first download the word dictionary that maps each word to an id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# downloads the word dict to assets/\n",
    "!wget https://paddlenlp.bj.bcebos.com/data/senta_word_dict.txt -P assets/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the word dict and specify the pretrained model path. \n",
    "\n",
    "To obtain the pretrained weights, please train a bilstm model following the [tutorial](https://github.com/PaddlePaddle/models/tree/release/2.0-beta/PaddleNLP/examples/text_classification/rnn) and specify the final `.pdparams` file position in `PARAMS_PATH` below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_vocab(vocab_file):\n",
    "    \"\"\"Loads a vocabulary file into a dictionary.\"\"\"\n",
    "    vocab = {}\n",
    "    with open(vocab_file, \"r\", encoding=\"utf-8\") as reader:\n",
    "        tokens = reader.readlines()\n",
    "    for index, token in enumerate(tokens):\n",
    "        token = token.rstrip(\"\\n\").split(\"\\t\")[0]\n",
    "        vocab[token] = index\n",
    "    return vocab\n",
    "\n",
    "PARAMS_PATH = \"assets/final.pdparams\"\n",
    "VOCAB_PATH = \"assets/senta_word_dict.txt\"\n",
    "\n",
    "vocab = load_vocab(VOCAB_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the BiLSTM model using **paddlenlp.models** and load pretrained weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import paddlenlp as ppnlp\n",
    "model = ppnlp.models.Senta(\n",
    "        network='bilstm',\n",
    "        vocab_size=len(vocab),\n",
    "        num_classes=2)\n",
    "\n",
    "state_dict = paddle.load(PARAMS_PATH)\n",
    "model.set_dict(state_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the `IntGradNLPInterpreter`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "ig = it.IntGradNLPInterpreter(model, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the reviews that we want to analyze. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = [\n",
    "    '这个宾馆比较陈旧了，特价的房间也很一般。总体来说一般',\n",
    "    '作为老的四星酒店，房间依然很整洁，相当不错。机场接机服务很好，可以在车上办理入住手续，节省时间。'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a preprocessing function that processes a list of raw strings into model inputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jieba\n",
    "def preprocess_fn(data):\n",
    "    texts = []\n",
    "    seq_lens = []\n",
    "    for text in data:\n",
    "        tokens = \" \".join(jieba.cut(text)).split(' ')\n",
    "#         tokens = text.split()\n",
    "        ids = []\n",
    "        unk_id = vocab.get('[UNK]', None)\n",
    "        for token in tokens:\n",
    "            wid = vocab.get(token, unk_id)\n",
    "            if wid:\n",
    "                ids.append(wid)\n",
    "        texts.append(ids)\n",
    "        seq_lens.append(len(ids))\n",
    "\n",
    "    pad_token_id = 0\n",
    "    max_seq_len = max(seq_lens)\n",
    "    for index, text in enumerate(texts):\n",
    "        seq_len = len(text)\n",
    "        if seq_len < max_seq_len:\n",
    "            padded_tokens = [pad_token_id for _ in range(max_seq_len - seq_len)]\n",
    "            new_text = text + padded_tokens\n",
    "            texts[index] = new_text\n",
    "        elif seq_len > max_seq_len:\n",
    "            new_text = text[:max_seq_len]\n",
    "            texts[index] = new_text\n",
    "    texts = paddle.to_tensor(texts)\n",
    "    texts.stop_gradient = False\n",
    "    seq_lens = paddle.to_tensor(seq_lens)\n",
    "    seq_lens.stop_gradient = False\n",
    "    return texts, seq_lens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, we `interpret` reviews and grab weights for each token.\n",
    "\n",
    "Since the output gradients are not grouped by reviews due to the LoDTensor inputs, we use the LoD information to group them into a list of lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache /tmp/jieba.cache\n",
      "Loading model cost 0.837 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    }
   ],
   "source": [
    "pred_labels, pred_probs, avg_gradients = ig.interpret(\n",
    "    preprocess_fn(reviews),\n",
    "    return_pred=True,\n",
    "    visual=True)\n",
    "\n",
    "sum_gradients = np.sum(avg_gradients, axis=-1).tolist()\n",
    "\n",
    "new_array = []\n",
    "for i in range(len(reviews)):\n",
    "    new_array.append(\n",
    "        list(zip(\" \".join(jieba.cut(reviews[i])).split(' '), sum_gradients[i])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For visualizasion purposes, word weights in each review are normalized to better illustrate differences between weights. Results for each review is stored in a list by making use of the `VisualizationTextRecord`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table width: 100%><tr><th>True Label</th><th>Predicted Label (Prob)</th><th>Target Label</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>0 (0.73)</b></text></td><td><text style=\"padding-right:2em\"><b>0</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 这个                        </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 宾馆                        </font></mark><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 比较                        </font></mark><mark style=\"background-color: hsl(0, 75%, 82%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 陈旧                        </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 了                        </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 特价                        </font></mark><mark style=\"background-color: hsl(0, 75%, 93%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 的                        </font></mark><mark style=\"background-color: hsl(0, 75%, 94%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 房间                        </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 也                        </font></mark><mark style=\"background-color: hsl(120, 75%, 87%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 很                        </font></mark><mark style=\"background-color: hsl(0, 75%, 80%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 一般                        </font></mark><mark style=\"background-color: hsl(0, 75%, 96%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 。                        </font></mark><mark style=\"background-color: hsl(120, 75%, 81%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 总体                        </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 来说                        </font></mark><mark style=\"background-color: hsl(0, 75%, 83%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 一般                        </font></mark></td><tr><tr><td><text style=\"padding-right:2em\"><b>0</b></text></td><td><text style=\"padding-right:2em\"><b>1 (0.73)</b></text></td><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><mark style=\"background-color: hsl(120, 75%, 94%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 作为                        </font></mark><mark style=\"background-color: hsl(120, 75%, 91%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 老                        </font></mark><mark style=\"background-color: hsl(0, 75%, 91%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 的                        </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 四星                        </font></mark><mark style=\"background-color: hsl(0, 75%, 96%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 酒店                        </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(0, 75%, 95%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 房间                        </font></mark><mark style=\"background-color: hsl(120, 75%, 82%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 依然                        </font></mark><mark style=\"background-color: hsl(120, 75%, 90%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 很                        </font></mark><mark style=\"background-color: hsl(120, 75%, 93%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 整洁                        </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(120, 75%, 90%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 相当                        </font></mark><mark style=\"background-color: hsl(120, 75%, 72%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 不错                        </font></mark><mark style=\"background-color: hsl(0, 75%, 96%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 。                        </font></mark><mark style=\"background-color: hsl(0, 75%, 96%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 机场                        </font></mark><mark style=\"background-color: hsl(0, 75%, 89%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 接机                        </font></mark><mark style=\"background-color: hsl(120, 75%, 97%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 服务                        </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 很                        </font></mark><mark style=\"background-color: hsl(120, 75%, 90%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 好                        </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 可以                        </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 在                        </font></mark><mark style=\"background-color: hsl(120, 75%, 93%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 车上                        </font></mark><mark style=\"background-color: hsl(0, 75%, 99%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 办理                        </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 入住                        </font></mark><mark style=\"background-color: hsl(0, 75%, 91%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 手续                        </font></mark><mark style=\"background-color: hsl(0, 75%, 98%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> ，                        </font></mark><mark style=\"background-color: hsl(0, 75%, 97%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 节省时间                        </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                         line-height:1.75\"><font color=\"black\"> 。                        </font></mark></td><tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_labels = [1, 0]\n",
    "recs = []\n",
    "for i, l in enumerate(new_array):\n",
    "    words = [t[0] for t in l]\n",
    "    word_importances = [t[1] for t in l]\n",
    "    word_importances = np.array(word_importances) / np.linalg.norm(\n",
    "        word_importances)\n",
    "    pred_label = pred_labels[i]\n",
    "    pred_prob = pred_probs[i]\n",
    "    true_label = true_labels[i]\n",
    "    interp_class = pred_label\n",
    "    if interp_class == 0:\n",
    "        word_importances = -word_importances\n",
    "    recs.append(\n",
    "        VisualizationTextRecord(words, word_importances, true_label,\n",
    "                                pred_label, pred_prob, interp_class))\n",
    "\n",
    "visualize_text(recs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above cell's output is similar to the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='assets/int_grad_nlp_viz.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "firstEnv",
   "language": "python",
   "name": "firstenv"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
